Head Impact Exposure in Youth Football: High School Ages 14 to 18 Years and Cumulative Impact Analysis

Transcription

1 Annals of Biomedical Engineering (Ó 2013) DOI: /s z Head Impact Exposure in Youth Football: High School Ages 14 to 18 Years and Cumulative Impact Analysis JILLIAN E. URBAN, 1,2 ELIZABETH M. DAVENPORT, 1,2 ADAM J. GOLMAN, 1,2 JOSEPH A. MALDJIAN, 2,3 CHRISTOPHER T. WHITLOW, 2,3,5 ALEXANDER K. POWERS, 2,4,6 and JOEL D. STITZEL 1,2,6 1 Virginia Tech Wake Forest University School of Biomedical Engineering and Sciences, Winston-Salem, NC 27157, USA; 2 Wake Forest School of Medicine, Medical Center Blvd., Winston-Salem, NC 27157, USA; 3 Department of Radiology (Neuroradiology), Wake Forest School of Medicine, Winston-Salem, NC 27157, USA; 4 Department of Neurosurgery, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA; 5 Translational Science Institute, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA; and 6 Childress Institute for Pediatric Trauma, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA (Received 1 March 2013; accepted 28 June 2013) Associate Editor Peter E. McHugh oversaw the review of this article. Abstract Sports-related concussion is the most common athletic head injury with football having the highest rate among high school athletes. Traditionally, research on the biomechanics of football-related head impact has been focused at the collegiate level. Less research has been performed at the high school level, despite the incidence of concussion among high school football players. The objective of this study is to twofold: to quantify the head impact exposure in high school football, and to develop a cumulative impact analysis method. Head impact exposure was measured by instrumenting the helmets of 40 high school football players with helmet mounted accelerometer arrays to measure linear and rotational acceleration. A total of 16,502 head were collected over the course of the season. Biomechanical data were analyzed by team and by player. The median impact for each player ranged from 15.2 to 27.0 g with an average value of 21.7 (±2.4) g. The 95th percentile impact for each player ranged from 38.8 to 72.9 g with an average value of 56.4 (±10.5) g. Next, an impact exposure metric utilizing concussion injury risk curves was created to quantify cumulative exposure for each participating player over the course of the season. Impacts were weighted according to the associated risk due to linear acceleration and rotational acceleration alone, as well as the combined probability (CP) of injury associated with both. These risks were summed over the course of a season to generate risk weighted cumulative exposure. The impact frequency was found to be greater during games compared to practices with an average number of per session of 15.5 and 9.4, respectively. However, the median cumulative risk weighted exposure based on combined probability was found to be greater for practices vs. games. These data will provide a metric that may be used to better understand the Address correspondence to Joel D. Stitzel, Wake Forest School of Medicine, Medical Center Blvd., Winston-Salem, NC 27157, USA. Electronic mail: jdstitzel@gmail.com, jstitzel@wakehealth.edu cumulative effects of repetitive head, injury mechanisms, and head impact exposure of athletes in football. Keywords Biomechanics, Brain injury, Concussion, Football, Pediatric, Youth, Helmet, Risk, High school. INTRODUCTION Sports-related concussion is the most common athletic head injury. 17,34 Currently, football is noted as having the highest concussion rate in high school athletes compared to other contact sports such as soccer, basketball, and hockey. 23 It is estimated that nearly 1.1 million students play high school football in the United States, 1 100,000 players participate in college football, 21 and 2000 players participate in professional football. 11 With such a large population participating in the sport, it is very important to understand head impact exposure in the context of the risk associated with different levels of impact in order to adequately estimate cumulative risk over the course of a practice, game, season, or lifetime. Traditionally, research on the biomechanics of football-related head impact has been focused at the collegiate level. 8 10,13,27,28,30 Less research has been performed in the high school and youth population, 3 5,11,32 despite the incidence of concussion among high school football players. 19 Approximately 5.6% (over 70,000) of high school football players and 4.4% (over 4000) Division I college football players sustain concussions in a given year. 19 Approximately 15% of Ó 2013 Biomedical Engineering Society

2 URBAN et al. the concussions followed a previous concussion in the same season. 19 However, these values do not reflect high rates of underreporting estimated in several studies. Underreporting rates are difficult to determine but range from 1 in 2 to 1 in 10 concussions. 22,24,29 The first analysis of head at different levels of play was conducted by Schnebel et al. 32 In this study, one high school team was fitted with helmets equipped with the Head Impact Telemetry (HIT) System during practices and games. The purpose of this study was to analyze the HIT System impact and kinematic data to characterize the type of session, playing position, and location of head impact and compare to head occurring in National Collegiate Athletic Association (NCAA) Division I football. Schnebel et al. found a higher frequency of highlevel at the collegiate level compared to high school, but reported little comparative analysis. Broglio et al. later conducted multiple analyses from consecutive seasons of high school athletes and found that the mean acceleration was 24.7 g, which was comparatively higher than values reported at the collegiate level (22.25 g). 5 These data were obtained using the same methods as Schnebel et al.; however, the differences between high school and college athletes were not statistically significant. The data from the Broglio study included 271 exceeding 70 g s and 78 exceeding 98 g s. In this group, there were 5 diagnosed concussions. Therefore, the authors discussed the potential need for a different mild traumatic brain injury (mtbi) threshold for high school athletes. While a threshold based operational definition of concussion is outdated due to improved understanding of concussion risk, 30 the motivation to study head impact exposure of high school athletes is still present. Most recently, Broglio et al. reported cumulative burden of head in high school football with an average annual cumulative (summed) linear acceleration of 16,746 g, and summed rotational acceleration of 1,090,067 rad/s 2. 3 These values were found to be approximately 55% lower than those measured at the collegiate level. However, linear (g s) or rotational (rad/s 2 ) unweighted summation based metrics ignore the nonlinear relationship between peak acceleration level and concussion risk. In that sense, they may give a misleading picture of cumulative exposure for individuals or teams for many different facets of football. These may include player or team level, position, practice vs. game statistics, season, and career differences which may be very large. The objective of this study is to collect and quantify head impact exposure data in high school football athletes. To this end, a novel cumulative exposure metric is developed and results are presented that utilize this metric with four different published analytical risk functions. These include: linear resultant acceleration (developed by Pellman et al.), 26 linear resultant acceleration (developed by Rowson et al.), 30 rotational resultant acceleration, 31 and combined probability (linear and rotational) resultant accelerations. 29 These are used to elucidate individual player and team-based exposure associated with practices and games for an entire season of football. This study adds to the ongoing investigation of head impact biomechanics in high school football, and introduces a new cumulative exposure metric that can be used for similar analyses at all levels of play. The metric developed may help researchers better understand the longitudinal effects of on the brain from youth to longer football careers. METHODS Data Collection The study protocol was approved by the Wake Forest School of Medicine Institutional Review Board and participant assent or consent and parental consent were appropriately obtained. Impact data were collected for the entire season, including preseason practices and scrimmages, regular season practices and games, and playoff practices and games. Head impact exposure was measured by instrumenting the helmets of high school football players with the Head Impact Telemetry (HIT) System head acceleration measurement device. 7,18 Each player participating in the study was provided a Riddell Revolution or Riddell Revolution Speed helmet instrumented with the HIT System. The HIT System has been extensively described in the previous literature. 2 9,13,28,30,32 For this study, the HIT System included a sideline base unit with a laptop computer connected to a radio receiver and an encoder unit for each helmet. This system collects impact data on the sidelines from each encoder equipped with six single-axis accelerometers. Data acquisition occurred each time an instrumented helmet received an impact where an accelerometer exceeded 14.4 g. The recorded impact includes 40 ms of data, including 8 ms of pretrigger data. The data is wirelessly transmitted to the sideline computer where kinematic linear and rotational accelerations are computed, which can be analyzed in terms of the peak g s, direction of impact, or other biomechanical indicators. All data were screened to remove any that did not result from the helmet being worn on the players head during play (i.e., dropped helmets). A complete description of the processing algorithm and validation has been previously described. 7 All rotational acceleration data were processed as per Rowson et al. 31

3 Head Impact Exposure in Youth Football Exposure Measurement The Weibull probability density function (pdf) has been previously fit to helmeted impact exposure data, described in Rowson et al. 30 The Weibull pdf is demonstrated in Eq. (1) where a is the scale parameter, b is the shape parameter, h is the threshold parameter, and x is the peak resultant linear or rotational head acceleration. The Weibull parameters a, b, and h are calculated from the Weibull distribution fit for each player s linear and rotational head acceleration from practices and games, separately. This was integrated over the respective acceleration to calculate the Weibull cumulative density function (cdf). bðx hþb 1 x h W pdf ¼ a b e ð a Þ b ð1þ In the event that a player s were not collected during a session due to a battery failure or because of late addition to the study, the player s missing accelerations were calculated from the player s using a Weibull distribution-based model. First, the average number of for that player in that respective session type (practice or game) was calculated. Next, this number was used to evenly sample the player s Weibull cdf, resulting in an exposure-calibrated prediction of head accelerations to replace the empty data set for that season separately for games and practices. The linear and rotational head accelerations for the complete season were compiled from both the actual accelerations and the sampled accelerations. Sampled accelerations accounted for less than 5% of data. Cumulative head impact exposure represents the concussion risk weighted sum of head as measured by peak resultant linear and/or rotational acceleration. It may be measured over the course of any particular time period and activity for a particular individual or group. The risk of concussion for each impact for each player was calculated using four different risk functions previously described in the literature. The four risk functions (Figs. 1 and 2) include the logistic regression equations and regression coefficients (Table 1): (1) professional football based on linear acceleration, 26 (2) collegiate football based on linear acceleration, 30 (3) rotational acceleration, 31 and (4) the combined probability (CP) from linear and rotational acceleration. 29 Risks associated with each head impact for each player were summed to compute the risk weighted cumulative exposure (RWE) for the season. For comparative purposes, this was repeated separately for all four risk functions, and the RWE calculated using each is referred to as RWE Pellman, RWE Linear, RWE Rotational, and RWE CP, respectively (Table 2). A non-parametric Wilcoxon test was utilized to compare differences in player-specific cumulative exposure between practices and games. Additionally, the RWE score for each player from each respective risk function was summed to calculate the team or season RWE. The data collected for this study was analyzed by impact frequency, impact location, and impact magnitude for individual high school football players. RESULTS A total of 40 high school players participated in this study. The average age of the participants at the beginning of the season was 17.1 years and ranged from 15.8 to 18.5 years. The participants in the study had an average height of ± 6.1 cm and an average weight of 89.0 ± 16.3 kg. A total of 16,502 were measured in 33 practices and 14 games, two of which were scrimmages. One player was excluded from the analysis due to an orthopedic injury that occurred within the first week of the season. The FIGURE 1. Injury risk as a function of (left) linear acceleration 26,27 and (right) rotational acceleration. 31

4 URBAN et al. median and 95th percentile linear head acceleration and rotational head acceleration for each player is reported in Table 3. The linear accelerations recorded for the season ranged from 10.0 to g. The data was highly right skewed with a median value of 21.9 g and 95th percentile value of 57.6 g (Fig. 3). For 33 practices, there were a total of The median linear acceleration for practices was 21.5 g, with 95th percentile value of 53.7 g. For 14 games, there were 7335 with a median linear acceleration value of 22.4 g and 95th percentile value of 62.1 g. There were 76 (0.46%) greater than the average linear acceleration value of 98 g s associated with concussion. 15,28 The average median linear acceleration for each player was 21.7 g (±2.36 g) with a range of 15.2 to 27.0 g. The average 95th percentile impact for each player was 56.4 g (±10.5 g) with a range of 38.8 to 72.9 g. The number of exceeding various percentile thresholds are provided in Table 4. The rotational accelerations for the season ranged from 2.9 to 7,701 rad/s 2. The data was, again, highly right skewed with a median value of 973 rad/s 2 and 95th percentile value of 2,481 rad/s 2 (Fig. 3). The collected during practice had a median rotational acceleration value of 942 rad/s 2 and 95th percentile value of 2,263 rad/s 2. The collected during games vs. practices demonstrate that rotational accelerations are higher for games with a median value of 1,013 rad/s 2 and 95th percentile value of 2,743 rad/s 2. The average median rotational acceleration amongst players was 953 rad/s 2 (±132 rad/s 2 ), ranging from 685 to 1232 rad/s 2. The average 95th percentile impact amongst players was 2519 rad/s 2 (±536 rad/s 2 ), ranging from 1855 to 3701 rad/s 2. The number of exceeding various percentile thresholds are provided in Table 4. The distribution of total number of for practices and games was right skewed and the median and 95th percentile values for each were analyzed. The median (and 95th percentile) of the total number of head during all practices and games was 185 (541) and 138 (610), respectively. The median (and 95th percentile) of the total number of head for all team sessions is 340 (1012) with the total number of ranging from 129 to 1258 per player. The number of per practice and per game was an average (and 95th percentile) value of 9.4 (19.0) and 15.5 (43.6), respectively. A t test assuming unequal variances demonstrated the average number of per player for games was significantly greater than for practices (p = ). Table 5 demonstrates the median and inter-quartile range of the number of received for each player at various thresholds (30, 40, 60, 80, and 100 g). TABLE 2. Risk Weighted Cumulative Exposure (RWE) equations, where a L is the measured peak linear acceleration, a R is the measured peak rotational acceleration, and n hits is the number of head in a season for a given player. FIGURE 2. Combined probability of concussion contour given from the combined linear and rotational acceleration from Rowson et al. Risk function(s) Pellman, Linear Rotational Combined Probability Equation RWE Pellman;Linear ¼ P nhits i¼1 Rða LÞ i RWE Rotational ¼ P nhits i¼1 Rða RÞ i RWE CP ¼ P nhits i¼1 CPða L; a R Þ i TABLE 1. Logistic regression equations and regression coefficients of the four injury risk functions utilized in the prediction of injury, where a and b are the regression coefficients and x is the measured acceleration for the Pellman, linear, and rotational risk functions. Equation Logistic regression equation Risk function Regression coefficients 1 (2) Ra ½ Š ¼ Linear (NFL) a = , b = þe aþbx Linear (Collegiate) a = , b = Rotational a = , b = (3) CP ¼ 1þe ðb o þb 1 aþb 2 aþb 3 aaþ Combined Probability (CP) b 0 = 210.2, b 1 = , b 2 = , b 3 = 29.2E207 b 0, b 1, b 2, and b 3 are the regression coefficients, a is the measured linear acceleration, and a is the measured rotational acceleration for the combined probability risk function.

5 Head Impact Exposure in Youth Football TABLE 3. Median and 95th percentile linear head acceleration and rotational head acceleration per player in ascending order by median linear head acceleration. Player number Linear acceleration (g) Rotational acceleration (rad/s 2 ) Median 95th percentile Median 95th percentile The highest percentage of occurred to the front of the head (45.3%), followed by the back (21.8%), and top (14.9%). Similarly, 45.7% of game occurred to the front of the helmet and 45.0% of during practices occurred to the front of the helmet. The impact location with the highest median peak linear acceleration for a single player was the top of the head with a median value of 34.6 g, for a player with a 95th percentile value of 91.2 g. The impact location with the highest median rotational acceleration for a single player was the back of the head with a value of 1483 rad/s 2, and respective 95th percentile value of 3535 rad/s 2 for the given player. The results of the calculated RWE metric for each risk function are provided in Table 6. The data provided includes median, 95th percentile, minimum, and maximum RWE for each player for each risk function. The team RWE is additionally provided, which is the sum of the RWEs measured for each player for the season. The results of the multiple risk function analysis demonstrate high variability in the estimated exposure for the season based on the contribution of linear and/or rotational acceleration and the given risk function. The RWE Linear, RWE Rotational, and RWE CP values were analyzed by session activity (practices vs. games). Themedianriskweightedcumulative exposure for practice were RWE Linear = , RWE Rotational =0.0490, RWE CP = and for games were RWE Linear = , RWE Rotational = , RWE CP = These data suggest a higher cumulative exposure to linear accelerations during practice and slightly higher exposure to rotational accelerations during games, however no statistical significance was observed (p = 0.06 and p = 0.60, respectively). Overall RWE CP demonstrates that cumulative exposure from practices is one-third greater than that from games, however no statistical significance was observed (p = 0.47). DISCUSSION The goal of this study was to quantify head impact exposure in a season of high school football and develop a novel cumulative exposure metric to better understand these data. Head impact exposure has been extensively studied in the collegiate population with fewer studies investigating head sustained at the high school level. This study is a vital addition to previous studies of head impact exposure in football and is a key step toward understanding the risk weighted cumulative head impact exposure in a season of football at each level of play. The frequency and severity of observed are comparable to those observed at the collegiate level and consistent with data collected from different high school football data sets. The median linear head acceleration value (21.9 g) measured in this study is similar to those values reported by Broglio et al. (21.0 g) and Eckner et al. (20.5 g) at the high school level. 4,14 The median rotational head acceleration from this study (973 rad/s 2 ) was greater than the median value previously reported by Broglio et al. (903 rad/s 2 ). The distribution of median linear accelerations for all individual players in this study was highly variable with average median acceleration ranging from 15.2 to 27.0 g. The median linear head acceleration value reported at the collegiate level by Rowson et al. is 18 g suggesting more frequent

6 URBAN et al. FIGURE 3. Empirical cumulative density function (CDF) of linear (left) and rotational (right) acceleration. Each player CDF is represented in gray and the team CDF is represented in black. TABLE 4. Peak linear and rotational resultant acceleration percentile values and the measured number of above each threshold. Percentile Linear acceleration value (g) Number of above Rotational acceleration value (rad/s 2 ) Number of above higher severity occur at the high school level for many athletes. 28 The median rotational head acceleration value for each player s impact distribution in this study ranged from 685 to 1232 rad/s 2. The distribution of by impact location reveals that 45% of at the high school level occur to the front of the helmet and this is consistent between games and practices. This is also consistent with locations reported by Broglio et al. and Mihalik et al. for the high school level, as well as several other studies reported at the collegiate level. 4,5,25 One alarming result that has garnered attention throughout the football literature is the frequency and severity of head to the top of the helmet. Broglio et al. reported mean linear head acceleration for various player positions ranging from 19 to 38 g. 5 In the current study, the highest median value for a single player was found to be at the top of the head with a median value of 34.6 g, and 95th percentile value of 91.2 g. This value was 13 g higher than the average team median for all and 10 g higher than the team median for top of the helmet (Fig. 4). Although the severity is increased for to this location, side with a higher rotational component have been found to be the most likely impact scenario to result in concussion. 20,33 The results of the multiple risk function analysis demonstrate variability in the exposure to head for the season based on the contribution of linear and/or rotational acceleration, as well as between players (Appendix Tables A1, A2, A3, and A4). The median cumulative exposure varied between practices and games. These data suggest a higher exposure to linear accelerations (RWE Linear ) during practice and slightly higher exposure to rotational accelerations (RWE Rotational ) during games. RWE CP revealed higher cumulative exposure overall for practices. Interestingly, the average number of per game were found to be higher, however the median exposure was greater during practices. This suggests that players are exposed to a greater proportion of high level during practice. Interestingly, just over 60% of the team had greater than 50% of total risk weighted cumulative exposure attributed to practice. Although no statistically significant difference in exposure was observed between practices and games, these data may inform and encourage teams and leagues to reduce exposure to head during

7 Head Impact Exposure in Youth Football TABLE 5. The median and inter-quartile range (IQR) of the number of exceeding various thresholds (30, 40, 60, 80, and 100 g) from each player for the complete season, as well as for practices and games Season Median IQR Median IQR Median IQR TABLE 6. Results of multiple risk function comparison presented by risk function. RWE equation Median 95th percentile Minimum Maximum Team season RWE RWE Pellman RWE Linear RWE Rotational RWE CP The team season RWE is the summation of the RWE for each player resulting in the summed risk of concussion for the entire team for all practices and games for the season for each respective risk function. FIGURE 4. location. Median linear acceleration measured by impact practices and teach proper tackling techniques to reduce exposure to resulting in higher concussion risk. The risk weighted cumulative exposure metric presented in this study (i.e., RWE) has a different goal than metrics based on the Athlete Exposure (A-E). 12 One A-E represents one athlete participating in a single practice or game. Injury rates defined using this technique are expressed based on occurrence rate as a result of participation in one practice or competition. 9,12,16,30 In the case of football, then, A-E based injury metrics are independent of playing time, the number of, and the severity of received per exposure for a given player. The frequency of concussions in football can be expressed based on A-E s, however A-E based metrics do not account for the variance in impact exposure through a single practice or game for a single athlete, nor do they account for the cumulative effects over the course of a season which may vary extensively by player or position. Another method of defining the cumulative exposure for a given player has been previously presented by Broglio et al. 3 This method directly sums the linear or rotational accelerations experienced for each athlete. Although this method captures the severity and frequency of on aggregate, it does not take into consideration the nonlinear relationship between acceleration and risk of concussion, which can have substantial effects on the overall exposure. The risk weighted cumulative exposure metric introduced in this study adjusts each impact s contribution to cumulative exposure according to its associated risk of injury. Exposure is therefore a product of each player or group s distribution of over a chosen activity and time period. RWE is different depending on the injury risk metric used, and is used to examine the cumulative exposure to each acceleration type (RWE Linear, RWE Rotational ) or to assess the combined contribution of linear and rotational accelerations (RWE CP ). The data provided within the appendix, includes the calculated RWE Pellman, RWE Linear, RWE Rotational,and RWE CP for each player. These data are useful to interpret within group variability for RWE. A value of interest is the risk weighted exposure per impact for

8 URBAN et al. each player, which represents a normalized value by which risk weighted exposure may be examined on an individual basis. These data are important to capture the risk weighted exposure independent of the number of for each player, which is representative of the average severity for that player. One of the more interesting characteristics to study is the variation in severity between the highest and lowest exposure per impact players in games and/or practices. For example, the RWE Linear data show an eightfold variation in the exposure per impact for practices. Some players have increased exposure during games with as high as a fivefold variation in exposure per impact. Additionally, there is a 6.5-fold variation in the exposure per impact for practices and a threefold variation in the exposure per impact for games for the RWE Rotational data. Lastly, recorded values for RWE CP reveal a 22-fold variation in the exposure per impact for practices and a 47-fold variation in the exposure per impact for games between players. Since the exposure metric used is risk-weighted, the players with a higher RWE per impact may reveal exposure to a greater proportion of high magnitude compared to those who have a lower value. The variability in average exposure per impact that is captured when using a risk-weighted exposure metric may not be captured in a summed accelerationbased metric. Additionally, these types of analyses may provide further insight into position and player-specific exposure throughout a season of football. If RWE exceeds one for a given risk function, it would imply that the risk function predicted at least a single concussion over the course of the season. All the assumptions inherent in the risk function apply to the cumulative exposures calculated, including assumptions about underreporting. The Pellman risk function 26 for linear acceleration dramatically overestimates the total risk weighted cumulative exposure resulting in a median RWE Pellman per player of 19.4 and a team season RWE Pellman value of 1,007. This might be used to argue that each player would sustain 19.4 concussions and the team would sustain approximately 1,007 concussions over the course of the season. This is further evidence that the underlying risk function for RWE Pellman overestimates risk for each impact. More recent linear acceleration based risk functions more appropriately estimate risk and the associated risk weighted cumulative exposure. Caution, however, should be used when interpreting RWE as the estimated number of concussions. Though it is based on a risk-weighted summation of peak resultant accelerations, a very high number of very low risk may appear to give the same RWE as a smaller number of very high risk. In this sense, the likelihood that the person with a smaller number of high risk will have the calculated number of concussions is likely higher. However, risk weighted cumulative exposure is intended to address the importance of all, given that there is no established dichotomous threshold associated with damage due to smaller vs. larger. Traditionally linear and rotational acceleration have been evaluated independently. However, more recently studies have demonstrated that a combined metric with several biomechanical inputs may be more predictive than a single measure, particularly one that includes both linear and rotational acceleration. 4,18 The combined probability of the risk of concussion is a step toward a more comprehensive assessment of cumulative exposure. This risk function was developed based on a 109 underreporting rate, however it may be beneficial moving forward to perform comparative analyses utilizing varying underreporting rates that would affect the combined probability of concussion and/or RWE CP differently. Additionally, RWE Linear and RWE Rotational may be valuable metrics to be used in the understanding of the exposure to various acceleration types for an athlete. These data may be particularly useful in understanding the exposure for different playing positions in terms of linear and rotational acceleration, separately. This may be a valuable metric in capturing the lifetime exposure of an athlete and may also provide a better understanding of the role linear and rotational acceleration have on the mechanism of concussion and potential neurodegenerative changes. The results of this study will contribute to a better understanding of head impact exposure in high school football; however, certain limitations are present. Although a large number of have been collected at the high school level, analysis of position-specific distributions of was not conducted due to the need for further impact data for each player position. Also, not all players on the team were enrolled in the study, but 73% of the team consented. This introduces limitations on the exposure estimates for the entire team. The exposure methods utilized are based on various injury risk functions calculated previously by Rowson et al. and Pellman et al. 26,29 31 In the future, high school specific injury risk curves may be established and utilized in the calculation of RWE, especially as more data are collected from research groups using the HIT System to characterize head. However, it is estimated that the error introduced in using the risk curves described in this study is minimal, and RWE represents an improvement over A-E or acceleration sum-based measurements over the course of a season. There is a growing body of evidence that injury risk may also be directionally dependent. 35 The risk functions utilized in this study do not differ according to the direction of

9 Head Impact Exposure in Youth Football impact or axis of rotation, which may play an as yet undetermined role in injury risk, but a similar approach may incorporate such risk functions when they become available. Lastly, players receiving resulting in higher levels of risk substantially affected the total season RWE calculation. In the current sample, a large portion of the RWE contribution for the team came from just a few players and. Limiting the RWE calculation to particular percentile severity ranges, or otherwise excluding the highest, will substantially affect the RWE value and provide a potentially better metric for comparing populations. The way that the RWE is used to compare populations vs. individuals may be very important to understand age, team, playing time, or other independent variable based variances in exposure. This study has quantified head impact exposure in high school football, specifically focusing on the exposure to the risk of concussion for an entire football season. The cumulative risk associated with all measured for each player has not yet been quantified for any sport. A method has been developed to measure cumulative exposure to the risk of injury over the course of the season and this has been quantified for each player. This metric accounts for the number of player over the course of the season, as well as the severity of these. RWE Pellman was found to significantly over-estimate the total exposure risk. RWE Linear and RWE Rotational were found to capture the variability in exposure due to linear and/or rotational acceleration, as well as the exposure specific to session activity (i.e., practices vs. games). However, the combined risk weighted metric (CER CP ) may best capture the total linear and rotational exposure throughout the course of the season and into a lifetime. Establishment of a risk-based cumulative exposure metric is vital to understanding the biomechanical basis of head injury that may occur over the course of the football season, and potentially will have importance in correlating with potential preand post-season changes in the brain identified with magnetic resonance imaging, magnetoencephalography, and other neurological tests. Additionally, these metrics may also be beneficial for capturing the cumulative exposure of an athlete over a lifetime. The results presented in this paper contribute to the repository of head impact exposure data measured for various levels of play from youth football to the adult professional level which will further our understanding of the age-dependent biomechanics of head injury. These data ultimately have implications for assessment of helmet safety and improved helmet design, and ultimately can help make football a safer activity for millions of children, adolescents, and adults. ACKNOWLEDGMENTS The authors would like to thank the Reagan High School, especially Ashley Lake, ATC (Reagan High School), Corbin Ratcliffe, Lauren Smith and the football program. Thank you to Elizabeth Lillie and all those who contributed to the study development. Special thanks to the Childress Institute for Pediatric Trauma at Wake Forest Baptist Medical Center for providing support for this study. APPENDIX See appendix Tables A1, A2, A3, and A4. TABLE A1. RWE Pellman for each player calculated from practice, game, and total for the season with the linear NFL risk function. ID RWE Pellman practice RWE Pellman game RWE Pellman total Total Exposure per impact sample

10 URBAN et al. TABLE A1. continued. ID RWE Pellman practice RWE Pellman game RWE Pellman total Total Exposure per impact sample Players numbered in order of total number of in descending order. Players displayed in order of descending RWE Pellman Total Season. TABLE A2. RWE Linear for each player calculated from practice, game, and total for the season with the linear collegiate risk function. ID RWE Linear practice RWE Linear game RWE Linear total Total Exposure per impact sample

11 Head Impact Exposure in Youth Football TABLE A2. continued. ID RWE Linear practice RWE Linear game RWE Linear total Total Exposure per impact sample Players numbered in order of total number of in descending order. Players displayed in order of descending RWE Linear Total Season. TABLE A3. RWE Rotational for each player calculated from practice, game, and total for the season with the rotational risk function. ID RWE Rotational practice RWE Rotational game RWE Rotational total Total Exposure per impact sample

12 URBAN et al. TABLE A3. continued. ID RWE Rotational practice RWE Rotational game RWE Rotational total Total Exposure per impact sample Players numbered in order of total number of in descending order. Players displayed in order of descending RWE Rotational Total Season. TABLE A4. RWE CP for each player calculated from practice, game, and total for the season with the combined probability risk function. ID RWE CP practice RWE CP game RWE CP total Total Exposure per impact session Players numbered in order of total number of in descending order. Players displayed in order of descending RWE CP Total Season.

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